PTMs and half-lives
The half-life of a protein is the time it takes for the concentration of a protein to decrease by a half. Protein half-lives can be used as estimates of residence time of proteins in the cell.
Proteins that reside longer in the cell may be more susceptible to oxidative damage.
It is assumed that each protein only has one modification. The proteins with no modifications are not identified.
Protein half-lives for short lived proteins can be found here: Proteome-wide mapping of short-lived proteins in human cells - ScienceDirect (e-bronnen.be)
Protein half-lives of long-lived proteins can be found here: Systematic analysis of protein turnover in primary cells | Nature Communications
Protein half-lives of mouse proteins can be found here: An atlas of protein turnover rates in mouse tissues | Nature Communications
- CC_Results: Results for Cricoid Cartilage (Cartilage in manuscript)
Check that phosphorylation is the most abundant (literature).
Proteins with a short half-life
Proteins can have varying half-lives
What do the half-lives depend on?
How are they measured?
Below is a comparison of the distribution of the half-lives that was found in literature and the distribution of a subset of those half-lives in the proteins found in the dataset.
This is to check the proteins present in a particular half-life interval.
These are the modifications for a particular protein.
I want to remove the proteins with a very high number of log10(counts_norm_abund_len).
Outliers
Detecting the outliers:
Check that the outliers are removed:
What is the resulting distribution.
SRRM2_HUMAN
2752 amino acids.
There is a huge peak if you look at the data that is only normalised by the number of raw files.
Looking at SRRM2_HUMAN in more detail.
What are the most common modifications in this protein?
PTMs
Using genes from GenAge is ligit. Can continue doing that.
PTMs of interest:
PTMs that control autophagy
phosphorylation
ubiquitination -> need to use the new dataset
acetylation
oxPTMs
- you have a list of these
Methylation eg of histones
Acylation -> need to get this from the paper.
Phosphorylation
This is already without outliers
- Only the modification [21]Phospho is present here.
Splitting the dataset in a group with phosphorylation proteins and another group with all remaining proteins.
It is not necessary to include another density line with all of the proteins. You can just compare the two distributions.
Comparison
Testing whether the half-lives between groups are significantly different
Enriched proteins in both datasets:
Proteins that are only present in one of the dataframes.
Acetylation
- Filtered by the [1]Acetyl modification.
Ubiquitination
Ubiquitination has the classification ‘Other’. Take that as one group. The second group is all of the PTMs. 890 proteins overlap so you have 289 proteins taht are not ubiquitinated and have PTMs and we know their half-lives. These make up the second group.
Methylation
- Filtered by the [34]Methyl modification
Violin plots
Enrichment:
oxPTMs
This is only for proteins that are related to ageing.
All PTMs related to oxidative damage in general, not only oxidation.
Lysine acylations
Violin plot
AGEs
Violin plots
Binning
Hypothesis: The higher the half-life, the greater the number of PTMs.
Phosphorylation
oxPTMs
methylation
Ubiquitination, acetylation, lysine, AGEs
Check the number of proteins in each bin.
# A tibble: 5 × 2
hl_group protein_count
<chr> <int>
1 0-5 236
2 10-15 258
3 15-20 183
4 20+ 286
5 5-10 228oxPTMs
`summarise()` has grouped output by 'hl_group'. You can override using the
`.groups` argument.# A tibble: 15 × 3
# Groups: hl_group [5]
hl_group mod_group protein_count
<chr> <chr> <int>
1 0-5 - 235
2 0-5 Phosphorylated 166
3 0-5 oxPTMs 227
4 10-15 - 256
5 10-15 Phosphorylated 207
6 10-15 oxPTMs 254
7 15-20 - 182
8 15-20 Phosphorylated 147
9 15-20 oxPTMs 184
10 20+ - 286
11 20+ Phosphorylated 236
12 20+ oxPTMs 285
13 5-10 - 223
14 5-10 Phosphorylated 162
15 5-10 oxPTMs 221Proteins with a long half-life
Long-lived proteins can be used as estimators of chronological age. Long-lived proteins can be defined in different ways, for example based on the half-life of the protein when compared to the average half-life of proteins in the organism. In this case, long-lived proteins were obtained from the following study: paper. Proteins were classified as long-lived based on their degree of degradation during the experiment and therefore it was possible to discover new long-lived proteins (no a priori assumptions were made).
The study identified a list of long-lived proteins in rats, therefore human orthologs of these proteins were found.
Plot the data distributions
Outliers
Checking that the outliers have been removed.
All of the outliers have been removed.
Check the distribution of the half-lives:
Remove the proteins with very large half-lives:
Now the exact same thing but for `human_complete_hl_long`
PTMs
Phosphorylation
Violin plot:
Acetylation
Comparison:
Ubiquitination
Comparison
Methylation
oxPTMs
Comparing
Lysine acylations
AGEs
Violin plots:
Binning
oxPTMs:
